from grid_env_ideal_obs_repeat_task import *
from grid_agent import *
from checkpoint_utils import *
from maze_factory import *
from replay_config import *
import argparse
import json
import sys
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
from matplotlib.lines import Line2D
from sklearn.manifold import TSNE
import random
from sklearn.decomposition import PCA
from matplotlib.animation import FuncAnimation
from sklearn.cluster import KMeans
import threading
import mplcursors
from mpl_toolkits.mplot3d.art3d import Poly3DCollection

def progress_bar(current, total, barLength = 100):
    percent = float(current) * 100 / total
    arrow = '-' * int(percent/100 * barLength - 1) + '>'
    spaces = ' ' * (barLength - len(arrow))

    print('Progress: [%s%s] %d %%' % (arrow, spaces, percent), end='\r')
    sys.stdout.flush()

@partial(jax.jit, static_argnums=(3,))
def model_forward(variables, state, x, model):
    """ forward pass of the model
    """
    return model.apply(variables, state, x)

@jit
def get_action(y):
    return jnp.argmax(y)
get_action_vmap = jax.vmap(get_action)

# load landscape and states from file
def load_task(pth = "./logs/task.json"):
    # open json file
    with open(pth, "r") as f:
        data = json.load(f)
        landscape = data["data"]
        state = data["state"]
        goal = data["goal"]
        print("state: ", state)
        print("goal: ", goal)
        print("landscape: ", landscape)
    return landscape, state, goal

# save current landscape as json file
def save_current_task(landscape, start_x, start_y, goal_x, goal_y, pth = "./logs/landscape.json"):
    landscape_ = []
    for j in range(landscape[0].shape[0]):
        landscape_.append(int(landscape[0][j]))

    with open(pth, "w") as f:
        json.dump({"data": landscape_, 
                   "state": [start_x, start_y],
                   "goal": [goal_x, goal_y]}, f)


def rnn_run(x, mat_intr, bias1):
    intr_vector = np.dot(x, mat_intr) + bias1
    intr_vector = np.tanh(intr_vector)
    return intr_vector

# 计算雅克比矩阵
@jax.jit
def rnn_run_vector(x, mat_intr, bias1):
    intr_vector = jnp.dot(x, mat_intr) + bias1
    intr_vector = jnp.tanh(intr_vector)
    return intr_vector - x
jacobian_fun = jax.jacrev(rnn_run_vector)
jacobian_vmap = jax.jit(jax.vmap(jacobian_fun, in_axes=(0,None,None)))

@jax.jit
def vector_field_taylor_expansion(x, x0, jacobian, mat_intr, bias1):
    vector = jnp.dot(jacobian, x - x0) + rnn_run_vector(x0, mat_intr, bias1)
    return vector
vector_field_taylor_expansion_vmap = jax.jit(jax.vmap(vector_field_taylor_expansion, in_axes=(0,0,None,None,None)))

# run linear dynamical system using vector_field_taylor_expansion
def run_LDS_one_step(x, x0, dt, jacobian, mat_intr, bias1):

    x_dot = vector_field_taylor_expansion(x, x0, jacobian, mat_intr, bias1)
    x1 = x + x_dot * dt

    return x1.copy()

def compute_linearized_err_1X(sample_point, mat_intr, bias1):

    jacobian_of_x = jacobian_fun(jnp.array(sample_point), mat_intr, bias1)

    # 计算 jacobian_of_x 的所有特征值
    eigenvalues = np.linalg.eigvals(jacobian_of_x)
    # 对 eigenvalues 按照实部从大到小进行排序
    eigenvalues = np.sort(eigenvalues)[::-1]
    print("sorted eigenvalues of jacobian_of_x: ", eigenvalues)

    # 判断 eigenvalues 中是否含有实部大于零的元素
    if any(np.real(eigenvalues) > 0):
        print("存在实部大于零的项")
    else:
        print("不存在实部大于零的项")

    # 将 eigenvalues 所有实部绘制成 barchart
    fig, ax = plt.subplots()
    ax.bar(np.arange(len(eigenvalues)), np.real(eigenvalues))
    plt.show()

    sample_vector = rnn_run_vector(sample_point, mat_intr, bias1)
    sample_vector_len = np.linalg.norm(sample_vector)

    initial_state = sample_point
    current_state = initial_state.copy()
    current_state0 = current_state.copy()
    traj_len = 0

    next_state = rnn_run(initial_state, mat_intr, bias1)

    # while traj_len < sample_vector_len:
    for i in range(2000):
        current_state0 = current_state.copy()
        current_state = run_LDS_one_step(current_state, initial_state, 0.01, jacobian_of_x, mat_intr, bias1)
        delta_state = current_state - initial_state
        delta_state_len = np.linalg.norm(delta_state)
        traj_len += delta_state_len
        current_state0 = current_state.copy()

    diff_next_state_current_state = np.linalg.norm(next_state - current_state0)
    pears_corr = np.corrcoef(next_state, current_state0)[0, 1]

    current_state0_dot = vector_field_taylor_expansion(current_state0, initial_state, jacobian_of_x, mat_intr, bias1)
    next_state_vector = rnn_run_vector(next_state, mat_intr, bias1)

    pears_corr_vector = np.corrcoef(current_state0_dot, next_state_vector)[0, 1]

    print("pears_corr_vector: ", pears_corr_vector)

    return diff_next_state_current_state, pears_corr, current_state0, next_state


def ivf():

    """ parse arguments
    """
    rpl_config = ReplayConfig()

    parser = argparse.ArgumentParser()
    parser.add_argument("--model_pth", type=str, default=rpl_config.model_pth)
    parser.add_argument("--map_size", type=int, default=rpl_config.map_size)
    parser.add_argument("--task_pth", type=str, default=rpl_config.task_pth)
    parser.add_argument("--log_pth", type=str, default=rpl_config.log_pth)
    parser.add_argument("--nn_size", type=int, default=rpl_config.nn_size)
    parser.add_argument("--nn_type", type=str, default=rpl_config.nn_type)
    parser.add_argument("--show_kf", type=str, default=rpl_config.show_kf)
    parser.add_argument("--visualization", type=str, default=rpl_config.visualization)
    parser.add_argument("--video_output", type=str, default=rpl_config.video_output)
    parser.add_argument("--life_duration", type=int, default=rpl_config.life_duration)
    parser.add_argument("--start_i", type=int, default=rpl_config.start_i)
    parser.add_argument("--end_i", type=int, default=rpl_config.end_i)
    parser.add_argument("--load_data", type=int, default=rpl_config.load_data)

    args = parser.parse_args()

    rpl_config.model_pth = args.model_pth
    rpl_config.map_size = args.map_size
    rpl_config.task_pth = args.task_pth
    rpl_config.log_pth = args.log_pth
    rpl_config.nn_size = args.nn_size
    rpl_config.nn_type = args.nn_type
    rpl_config.show_kf = args.show_kf
    rpl_config.visualization = args.visualization
    rpl_config.video_output = args.video_output
    rpl_config.life_duration = args.life_duration
    rpl_config.start_i = args.start_i
    rpl_config.end_i = args.end_i
    rpl_config.load_data = args.load_data

    """ create landscape
    """
    random_task = True
    # check if file on rpl_config.task_pth exists
    if os.path.isfile(rpl_config.task_pth):
        random_task = False

    if random_task:
        landscape = generate_maze_pool(num_mazes=1, width=10, height=10)
        landscape = padding_landscapes(landscape, width=12, height=12)
    else:
        landscape, state, goal = load_task(pth = rpl_config.task_pth)
        landscape = [landscape]

    """ create grid env
    """
    start_time = time.time()
    GE = GridEnv(landscapes = landscape, width = 12, height = 12, num_envs_per_landscape = 1, reward_free=True)
    GE.reset()
    print("time taken to create envs: ", time.time() - start_time)

    if not random_task:
        # set states of GE
        GE.batched_states = GE.batched_states.at[0, 0].set(state[0])
        GE.batched_states = GE.batched_states.at[0, 1].set(state[1])
        # set goals of GE
        GE.batched_goals = GE.batched_goals.at[0, 0].set(goal[0])
        GE.batched_goals = GE.batched_goals.at[0, 1].set(goal[1])
        GE.init_batched_states, GE.init_batched_goals = jnp.copy(GE.batched_states), jnp.copy(GE.batched_goals)
        GE.batched_goal_reached = batch_compute_goal_reached(GE.batched_states, GE.batched_goals)
        GE.last_batched_goal_reached = jnp.copy(GE.batched_goal_reached)
        GE.concat_obs = get_ideal_obs_vmap_rf(GE.batched_envs, GE.batched_states, GE.batched_goals, GE.last_batched_goal_reached)

    concat_obs = GE.concat_obs

    print("concat_obs: ", concat_obs.shape)

    for w in range(1,10):
        for h in range(1,10):

            GE.batched_states = jnp.array([[w,h] for i in range(1)])

            if rpl_config.load_data == 0:

                """ load model
                """
                params = load_weights(rpl_config.model_pth)

                # get elements of params
                tree_leaves = jax.tree_util.tree_leaves(params)
                for i in range(len(tree_leaves)):
                    print("shape of leaf ", i, ": ", tree_leaves[i].shape)

                bias1 = np.array(tree_leaves[0])
                mat1 = np.array(tree_leaves[1])
                print("mat1.shape: ", mat1.shape)
                print("bias1: ", bias1)

                mat_obs = np.array(tree_leaves[1])[rpl_config.nn_size:rpl_config.nn_size+10,:]
                mat_intr = np.array(tree_leaves[1])[0:rpl_config.nn_size,:]
                print("mat_obs.shape: ", mat_obs.shape)
                
                """ create agent
                """
                if rpl_config.nn_type == "vanilla":
                    model = RNN(hidden_dims = rpl_config.nn_size)
                elif rpl_config.nn_type == "gru":
                    model = GRU(hidden_dims = rpl_config.nn_size)

                # check if param fits the agent
                if rpl_config.nn_type == "vanilla":
                    assert params["params"]["Dense_0"]["kernel"].shape[0] == rpl_config.nn_size + 10

                n_samples = 50
                k1 = npr.randint(0, 1000000)
                rnn_state = model.initial_state_rnd(n_samples, k1)
                rnn_state_old = rnn_state.copy()
                diff = jnp.abs(rnn_state - rnn_state_old)
                rnn_state_old = rnn_state.copy()
                diff_norm = jnp.linalg.norm(diff, axis=1)
                diff_norm_old = diff_norm.copy()
                norm_std = diff_norm.copy()

                rnn_state_init = rnn_state.copy()

                obs_zero = jnp.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0] for i in range(n_samples)])

                rnn_state_trajectory = []
                intr_field = []

                for t in range(rpl_config.life_duration):

                    progress_bar(t, rpl_config.life_duration)

                    intr_vector = np.dot(rnn_state, mat_intr) + bias1
                    intr_vector = np.tanh(intr_vector)
                    intr_field.append(intr_vector - rnn_state)

                    rnn_state_trajectory.append(np.array(rnn_state).copy())
                    
                    """ model forward 
                    """
                    # rnn_state, y1 = model_forward(params, rnn_state, obs_zero, model)
                    rnn_state = intr_vector
                    
                print(rnn_state.shape)
                print(norm_std.shape)

                # 将 rnn_state_trajectory 展开成 rnn_state_np 的形状
                rnn_state_trajectory_np = np.array(rnn_state_trajectory)
                rnn_state_trajectory_np = rnn_state_trajectory_np.reshape(-1, rnn_state_trajectory_np.shape[-1])
                print("shape of rnn_state_trajectory_np: ", rnn_state_trajectory_np.shape)

                # 将 intr_field 展开成 rnn_state_np 的形状
                intr_field_np = np.array(intr_field)
                intr_field_np = intr_field_np.reshape(-1, intr_field_np.shape[-1])
                print("shape of intr_field_np: ", intr_field_np.shape)

                # 对 rnn_state_np 进行 PCA
                intrinsic_pca = PCA()
                intrinsic_pca.fit(rnn_state_trajectory_np)
                rnn_state_trajectory_np_pca = intrinsic_pca.transform(rnn_state_trajectory_np)

                intr_field_np_pca = intrinsic_pca.transform(intr_field_np + intrinsic_pca.mean_)

                # 保存数据
                np.save("./logs/rnn_state_trajectory_np.npy", rnn_state_trajectory_np)

                print("shape of rnn_state_trajectory_np: ", rnn_state_trajectory_np.shape)
            
            elif rpl_config.load_data == 1:

                """ load model
                """
                params = load_weights(rpl_config.model_pth)
                
                # get elements of params
                tree_leaves = jax.tree_util.tree_leaves(params)
                for i in range(len(tree_leaves)):
                    print("shape of leaf ", i, ": ", tree_leaves[i].shape)

                bias1 = np.array(tree_leaves[0])
                mat1 = np.array(tree_leaves[1])
                print("mat1.shape: ", mat1.shape)
                print("bias1: ", bias1)

                mat_obs = np.array(tree_leaves[1])[rpl_config.nn_size:rpl_config.nn_size+10,:]
                mat_intr = np.array(tree_leaves[1])[0:rpl_config.nn_size,:]
                print("mat_obs.shape: ", mat_obs.shape)

                rnn_state_trajectory_np = np.load("./logs/rnn_state_trajectory_np.npy")

                # 对 rnn_state_np 进行 PCA
                intrinsic_pca = PCA()
                intrinsic_pca.fit(rnn_state_trajectory_np)
                rnn_state_trajectory_np_pca = intrinsic_pca.transform(rnn_state_trajectory_np)

                GE.concat_obs = get_ideal_obs_vmap_rf(GE.batched_envs, GE.batched_states, GE.batched_goals, GE.last_batched_goal_reached)
                IPF = np.dot(GE.concat_obs[0], mat_obs)

                intr_vector = np.dot(rnn_state_trajectory_np, mat_intr) + bias1
                intr_vector = np.tanh(intr_vector + IPF)
                intr_field = intr_vector - rnn_state_trajectory_np

                intr_field_np_pca = intrinsic_pca.transform(intr_field + intrinsic_pca.mean_)

                # print("shape of IPF: ", IPF.shape)

                img = GE.render()

            dim0 = 0
            dim1 = 1
            dim2 = 2

            # 将图像从 BGR 转换为 RGB 格式
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

            # 创建包含两个子图的图表
            fig = plt.figure(figsize=(15, 5))
            ax1 = fig.add_subplot(121, projection='3d')
            ax2 = fig.add_subplot(122)

            # 在第一个子图中显示您的三维图表
            ax1.scatter(np.array(rnn_state_trajectory_np_pca)[:, dim0], 
                        np.array(rnn_state_trajectory_np_pca)[:, dim1], 
                        np.array(rnn_state_trajectory_np_pca)[:, dim2],
                        s=10)
            ax1.quiver(rnn_state_trajectory_np_pca[:, dim0], rnn_state_trajectory_np_pca[:, dim1], rnn_state_trajectory_np_pca[:, dim2], 
                        intr_field_np_pca[:, dim0], intr_field_np_pca[:, dim1], intr_field_np_pca[:, dim2], color='r', length=0.2, arrow_length_ratio=0.3)

            # 在第二个子图中显示您的 OpenCV 图像
            ax2.imshow(img)

            # # 显示图表
            plt.show()



if __name__ == "__main__":

    ivf()